Technical Field
Driver behavior monitoring and prompting.
Description of the Related Art
Driver behavior is the leading cause of automobile crashes and modifying driver behavior is a focus of safety practitioners who implement safety programs to reduce crash rates. These efforts are of paramount importance to large fleets that are faced with the potential to lose millions of dollars in legal liabilities due to crashes caused by the drivers of their vehicles. One of the challenges to modifying driver behavior lies in the initial detection of unsafe driver behavior prior to the actuality of a crash occurrence on a driving record. Methods exist today to detect proxies for driver behavior through the monitoring of vehicle driving events such as speed (20130021148, 20130096731), hard-braking (20130096731, 20130274950), engine RPMs (20130245880), gear-shifting, lane departure warnings & roll stability activations. Other onboard event monitoring includes video capture of the driver to detect fatigue related events like nodding off, falling asleep and drowsiness. Meta-data can also be collected including weather data (20120221216) and geo-location (20130073114) information in efforts to filter out environmental factors affecting speed (bad weather, city congestion) that may skew individual vehicle event data (actual driving speed lower than average; safe driving speed lower than posted limits) that lead to faulty analysis of individual driving behavior.
Some previous work on driver behavior depends on deriving proxies for driver behavior based on onboard vehicle data inputs, and applying those proxies over a large time period and large population of drivers. Some previous work relies on an indirect association between deviations from broadly generalized averages and driver safety. Population statistics such as average speed, average gear changes, or average number of hard-braking events create baseline proxies for safe driving, and large deviations from averages are sometimes used to identify unsafe drivers for coaching. Hard braking events to avoid accidents with other vehicles cannot take into account the fault of other drivers. Drivers may drive faster on the same routes based only on the delivery schedule and whether it coincides with higher traffic volumes. All of these examples can produce data that may erroneously skew negative perceptions of a driver's safe habits when in fact the data, when taken in a fuller context, may point to the opposite. This is a potential shortcoming of uncontextualized vehicle event data being used as proxies for driver safety. It relies on the law of averages to balance out irregular sample data and highly variable conditions within population statistics without addressing the fact that individual sample data continues to be relied upon to identify high risk drivers.
Furthermore, a safety program that provides general training or coaching in response to a general comparison of individual driver behavior vs. a population of drivers fails to take into account the more powerful coaching opportunities that may be present in the moment, in the context of a specific driving decision. For example, many in-cab navigation systems will coach drivers to slow down if their speed exceeds the posted limit. This helps to remind drivers, if their attention has drifted, and also teaches drivers on regular routes about where speed limits change. With the exception of video monitoring for drowsiness, the state of the art contains little consideration of instantaneous driver coaching.
Some current methodologies often also collect vast amounts of uncontextualized data covering the entire drive time of each driver. There are large data transmission costs associated with these methodologies and they are most extreme when vehicle events include video data.
This is not to say that these methodologies are without merit, it is only to explain what may be their limitations.